A High-Reliability, High-Resolution Method for Land Cover Classification Into Forest and Non-forest

  • Roger Trias-Sanz
  • Didier Boldo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

We present several methods for per-region land-cover classification based on distances on probability distributions and whole-region probabilities. We present results on using this method for locating forest areas in high-resolution aerial images with very high reliability, achieving more than 95% accuracy, using raw radiometric channels as well as derived color and texture features. Region boundaries are obtained from a multi-scale hierarchical segmentation or from a registration of cadastral maps.

Keywords

Land Cover Normalize Difference Vegetation Index Land Cover Class Aerial Image Segmentation Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Roger Trias-Sanz
    • 1
    • 2
  • Didier Boldo
    • 1
  1. 1.Institut Géographique NationalSaint-MandéFrance
  2. 2.SIP-CRIP5Université René DescartesParisFrance

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